Recovering data within a unified storage element

ABSTRACT

Staging data on a storage element integrating fast durable storage and bulk durable storage, including: receiving, at a storage element integrating fast durable storage and bulk durable storage, a data storage operation from a host computer; storing data corresponding to the data storage operation within fast durable storage in accordance with a first data resiliency technique; and responsive to detecting a condition for transferring data between fast durable storage and bulk durable storage, transferring the data from fast durable storage to bulk durable storage in accordance with a second data resiliency technique.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplications Ser. No. 15/697,540, 15/697,566, and 15/697,521, filed Sep.7, 2017, and claims benefit of U.S. Provisional Patent Applications:62/518,551, filed Jun. 12, 2017, 62/589,524, filed Nov. 21, 2017, and62/631,933, filed Feb. 18, 2018.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 4 sets forth a flow chart illustrating an example method forrecovering data within a unified storage element according to someembodiments of the present disclosure.

FIG. 5 sets forth a flow chart illustrating an example method forrecovering data within a unified storage element according to someembodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for recovering data within aunified storage element in accordance with embodiments of the presentdisclosure are described with reference to the accompanying drawings,beginning with FIG. 1A. FIG. 1A illustrates an example system for datastorage, in accordance with some implementations. System 100 (alsoreferred to as “storage system” herein) includes numerous elements forpurposes of illustration rather than limitation. It may be noted thatsystem 100 may include the same, more, or fewer elements configured inthe same or different manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include may include mechanical or spinning hard disk,such as hard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller (e.g.,storage array controller 110A) of a storage system 100 may be designatedwith primary status (also referred to as “primary controller” herein),and other storage array controllers (e.g., storage array controller110A) may be designated with secondary status (also referred to as“secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an application specificintegrated circuit (‘ASIC’), a field programmable gate array (‘FPGA’), adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storage devicecontroller 119 a-d. In one embodiment, storage device controller 119 a-dmay be a CPU, ASIC, FPGA, or any other circuitry that may implementcontrol structures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119 a-d. Flash memory devices 120 a-n,may be presented to the controller 119 a-d as an addressable collectionof Flash pages, erase blocks, and/or control elements sufficient toallow the storage device controller 119 a-d to program and retrievevarious aspects of the Flash. In one embodiment, storage devicecontroller 119 a-d may perform operations on flash memory devices 120a-n including storing and retrieving data content of pages, arrangingand erasing any blocks, tracking statistics related to the use and reuseof Flash memory pages, erase blocks, and cells, tracking and predictingerror codes and faults within the Flash memory, controlling voltagelevels associated with programming and retrieving contents of Flashcells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119 a-d or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119 a-d.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119a-d, some amount of the RAM (e.g., RAM 121), and some amount of Flashmemory (e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119 a-d may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119 a-d from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b, or which may be provided separately. An alternative embodimentincludes a separate Flash memory (not shown) dedicated for use instoring the content of RAM 121. The storage device controller 119 a-dmay present a logical device over a PCI bus which may include anaddressable fast-write logical device, or a distinct part of the logicaladdress space of the Dual PCI storage device 118, which may be presentedas PCI memory or as persistent storage. In one embodiment, operations tostore into the device are directed into the RAM 121. On power failure,the storage device controller 119 a-d may write stored contentassociated with the addressable fast-write logical storage to Flashmemory (e.g., Flash memory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a Dual PCI storagedevice 118 (e.g., storage system 117) to directly address Flash memorypages and directly reprogram erase blocks from storage system componentsthat are external to the storage device through the PCI bus. Thepresentation may also allow one or more of the external components tocontrol and retrieve other aspects of the Flash memory including some orall of: tracking statistics related to use and reuse of Flash memorypages, erase blocks, and cells across all the Flash memory devices;tracking and predicting error codes and faults within and across theFlash memory devices; controlling voltage levels associated withprogramming and retrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119 a-d and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119 a-d. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to storage devicecontrollers 119a, 119b and 119c, 119d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage device controllers 119 a-d to journal inprogress operations to ensure the operations are not lost on a powerfailure, storage controller removal, storage controller or storagesystem shutdown, or some fault of one or more software or hardwarecomponents within the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128a and128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a-d maybe operable under direction from a storage controller 125 a tosynthesize and transfer data to be stored into Flash memory devices fromdata that has been stored in RAM (e.g., RAM 121 of FIG. 1C). Forexample, a recalculated version of RAM content may be transferred aftera storage controller has determined that an operation has fullycommitted across the storage system, or when fast-write memory on thedevice has reached a certain used capacity, or after a certain amount oftime, to ensure improve safety of the data or to release addressablefast-write capacity for reuse. This mechanism may be used, for example,to avoid a second transfer over a bus (e.g., 128 a, 128 b) from thestorage controllers 125 a, 125 b. In one embodiment, a recalculation mayinclude compressing data, attaching indexing or other metadata,combining multiple data segments together, performing erasure codecalculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119a, 119b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119 a-d may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage (s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIG. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Modes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., a fieldprogrammable gate array (FPGA). In this embodiment, each flash die 222has pages, organized as sixteen kB (kilobyte) pages 224, and a register226 through which data can be written to or read from the flash die 222.In further embodiments, other types of solid-state memory are used inplace of, or in addition to flash memory illustrated within flash die222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA (field programmable gate array), flash memory206, and NVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS.2B and 2C) on a PCIe (peripheral component interconnect express) boardin a chassis 138 (see FIG. 2A). The storage unit 152 may be implementedas a single board containing storage, and may be the largest tolerablefailure domain inside the chassis. In some embodiments, up to twostorage units 152 may fail and the device will continue with no dataloss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool_region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller (s) 246. Management of the control plane and the storageplane are treated independently, although parts may be physicallyco-located on the same blade. Each authority 168 effectively serves asan independent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources.

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or local area network (‘LAN’), or as some othermechanism capable of transporting digital information between thestorage system 306 and the cloud services provider 302. Such a datacommunications link 304 may be fully wired, fully wireless, or someaggregation of wired and wireless data communications pathways. In suchan example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using one or more data communications protocols.For example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using the handheld device transfer protocol(‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol(‘IP’), real-time transfer protocol (‘RTP’), transmission controlprotocol (‘TCP’), user datagram protocol (‘UDP’), wireless applicationprotocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides services tousers of the cloud services provider 302 through the sharing ofcomputing resources via the data communications link 304. The cloudservices provider 302 may provide on-demand access to a shared pool ofconfigurable computing resources such as computer networks, servers,storage, applications and services, and so on. The shared pool ofconfigurable resources may be rapidly provisioned and released to a userof the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services to the storage system 306 and users ofthe storage system 306 through the implementation of an infrastructureas a service (‘IaaS’) service model where the cloud services provider302 offers computing infrastructure such as virtual machines and otherresources as a service to subscribers. In addition, the cloud servicesprovider 302 may be configured to provide services to the storage system306 and users of the storage system 306 through the implementation of aplatform as a service (‘PaaS’) service model where the cloud servicesprovider 302 offers a development environment to application developers.Such a development environment may include, for example, an operatingsystem, programming-language execution environment, database, webserver, or other components that may be utilized by applicationdevelopers to develop and run software solutions on a cloud platform.Furthermore, the cloud services provider 302 may be configured toprovide services to the storage system 306 and users of the storagesystem 306 through the implementation of a software as a service(‘SaaS’) service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. The cloud services provider302 may be further configured to provide services to the storage system306 and users of the storage system 306 through the implementation of anauthentication as a service (‘AaaS’) service model where the cloudservices provider 302 offers authentication services that can be used tosecure access to applications, data sources, or other resources. Thecloud services provider 302 may also be configured to provide servicesto the storage system 306 and users of the storage system 306 throughthe implementation of a storage as a service model where the cloudservices provider 302 offers access to its storage infrastructure foruse by the storage system 306 and users of the storage system 306.Readers will appreciate that the cloud services provider 302 may beconfigured to provide additional services to the storage system 306 andusers of the storage system 306 through the implementation of additionalservice models, as the service models described above are included onlyfor explanatory purposes and in no way represent a limitation of theservices that may be offered by the cloud services provider 302 or alimitation as to the service models that may be implemented by the cloudservices provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. Public cloud and private clouddeployment models may differ and may come with various advantages anddisadvantages. For example, because a public cloud deployment involvesthe sharing of a computing infrastructure across different organization,such a deployment may not be ideal for organizations with securityconcerns, mission-critical workloads, uptime requirements demands, andso on. While a private cloud deployment can address some of theseissues, a private cloud deployment may require on-premises staff tomanage the private cloud. In still alternative embodiments, the cloudservices provider 302 may be embodied as a mix of a private and publiccloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat additional hardware components and additional software componentsmay be necessary to facilitate the delivery of cloud services to thestorage system 306 and users of the storage system 306. For example, thestorage system 306 may be coupled to (or even include) a cloud storagegateway. Such a cloud storage gateway may be embodied, for example, ashardware-based or software-based appliance that is located on premisewith the storage system 306. Such a cloud storage gateway may operate asa bridge between local applications that are executing on the storagearray 306 and remote, cloud-based storage that is utilized by thestorage array 306. Through the use of a cloud storage gateway,organizations may move primary iSCSI or NAS to the cloud servicesprovider 302, thereby enabling the organization to save space on theiron-premises storage systems. Such a cloud storage gateway may beconfigured to emulate a disk array, a block-based device, a file server,or other storage system that can translate the SCSI commands, fileserver commands, or other appropriate command into REST-space protocolsthat facilitate communications with the cloud services provider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. Such applications may takemany forms in accordance with various embodiments of the presentdisclosure. For example, the cloud services provider 302 may beconfigured to provide access to data analytics applications to thestorage system 306 and users of the storage system 306. Such dataanalytics applications may be configured, for example, to receivetelemetry data phoned home by the storage system 306. Such telemetrydata may describe various operating characteristics of the storagesystem 306 and may be analyzed, for example, to determine the health ofthe storage system 306, to identify workloads that are executing on thestorage system 306, to predict when the storage system 306 will run outof various resources, to recommend configuration changes, hardware orsoftware upgrades, workflow migrations, or other actions that mayimprove the operation of the storage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include storage resources308, which may be embodied in many forms. For example, in someembodiments the storage resources 308 can include nano-RAM or anotherform of nonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate. In some embodiments, the storage resources 308may include 3D crosspoint non-volatile memory in which bit storage isbased on a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. In some embodiments, the storageresources 308 may include flash memory, including single-level cell(‘SLC’) NAND flash, multi-level cell (‘MLC’) NAND flash, triple-levelcell (‘TLC’) NAND flash, quad-level cell (‘QLC’) NAND flash, and others.In some embodiments, the storage resources 308 may include non-volatilemagnetoresistive random-access memory (‘MRAM’), including spin transfertorque (‘STT’) MRAM, in which data is stored through the use of magneticstorage elements. In some embodiments, the example storage resources 308may include non-volatile phase-change memory (‘PCM’) that may have theability to hold multiple bits in a single cell as cells can achieve anumber of distinct intermediary states. In some embodiments, the storageresources 308 may include quantum memory that allows for the storage andretrieval of photonic quantum information. In some embodiments, theexample storage resources 308 may include resistive random-access memory(‘ReRAM’) in which data is stored by changing the resistance across adielectric solid-state material. In some embodiments, the storageresources 308 may include storage class memory (‘SCM’) in whichsolid-state nonvolatile memory may be manufactured at a high densityusing some combination of sub-lithographic patterning techniques,multiple bits per cell, multiple layers of devices, and so on. Readerswill appreciate that other forms of computer memories and storagedevices may be utilized by the storage systems described above,including DRAM, SRAM, EEPROM, universal memory, and many others. Thestorage resources 308 depicted in FIG. 3A may be embodied in a varietyof form factors, including but not limited to, dual in-line memorymodules (‘DIMMs’), non-volatile dual in-line memory modules (‘NVDIMMs’),M.2, U.2, and others.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306. The communications resources 310may be configured to utilize a variety of different protocols and datacommunication fabrics to facilitate data communications betweencomponents within the storage systems as well as computing devices thatare outside of the storage system. For example, the communicationsresources 310 can include fibre channel (‘FC’) technologies such as FCfabrics and FC protocols that can transport SCSI commands over FCnetworks. The communications resources 310 can also include FC overethernet (‘FCoE’) technologies through which FC frames are encapsulatedand transmitted over Ethernet networks. The communications resources 310can also include InfiniBand (‘IB’) technologies in which a switchedfabric topology is utilized to facilitate transmissions between channeladapters. The communications resources 310 can also include NVM Express(‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologiesthrough which non-volatile storage media attached via a PCI express(‘PCIe’) bus may be accessed. The communications resources 310 can alsoinclude mechanisms for accessing storage resources 308 within thestorage system 306 utilizing serial attached SCSI (‘SAS’), serial ATA(‘SATA’) bus interfaces for connecting storage resources 308 within thestorage system 306 to host bus adapters within the storage system 306,internet small computer systems interface (‘iSCSI’) technologies toprovide block-level access to storage resources 308 within the storagesystem 306, and other communications resources that that may be usefulin facilitating data communications between components within thestorage system 306, as well as data communications between the storagesystem 306 and computing devices that are outside of the storage system306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or moreapplication-specific integrated circuits (‘ASICs’) that are customizedfor some particular purpose as well as one or more central processingunits (‘CPUs’). The processing resources 312 may also include one ormore digital signal processors (‘DSPs’), one or more field-programmablegate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or otherform of processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform various tasks. The software resources314 may include, for example, one or more modules of computer programinstructions that when executed by processing resources 312 within thestorage system 306 are useful in carrying out various data protectiontechniques to preserve the integrity of data that is stored within thestorage systems. Readers will appreciate that such data protectiontechniques may be carried out, for example, by system software executingon computer hardware within the storage system, by a cloud servicesprovider, or in other ways. Such data protection techniques can include,for example, data archiving techniques that cause data that is no longeractively used to be moved to a separate storage device or separatestorage system for long-term retention, data backup techniques throughwhich data stored in the storage system may be copied and stored in adistinct location to avoid data loss in the event of equipment failureor some other form of catastrophe with the storage system, datareplication techniques through which data stored in the storage systemis replicated to another storage system such that the data may beaccessible via multiple storage systems, data snapshotting techniquesthrough which the state of data within the storage system is captured atvarious points in time, data and database cloning techniques throughwhich duplicate copies of data and databases may be created, and otherdata protection techniques. Through the use of such data protectiontechniques, business continuity and disaster recovery objectives may bemet as a failure of the storage system may not result in the loss ofdata stored in the storage system.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

Readers will appreciate that the various components depicted in FIG. 3Bmay be grouped into one or more optimized computing packages asconverged infrastructures. Such converged infrastructures may includepools of computers, storage and networking resources that can be sharedby multiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may minimizecompatibility issues between various components within the storagesystem 306 while also reducing various costs associated with theestablishment and operation of the storage system 306. Such convergedinfrastructures may be implemented with a converged infrastructurereference architecture, with standalone appliances, with a softwaredriven hyper-converged approach (e.g., hyper-converged infrastructures),or in other ways.

Readers will appreciate that the storage system 306 depicted in FIG. 3Bmay be useful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. Such AI applications may enable devices to perceive theirenvironment and take actions that maximize their chance of success atsome goal. Examples of such AI applications can include IBM Watson,Microsoft Oxford, Google DeepMind, Baidu Minwa, and others. The storagesystems described above may also be well suited to support other typesof applications that are resource intensive such as, for example,machine learning applications. Machine learning applications may performvarious types of data analysis to automate analytical model building.Using algorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may be also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. A GPU is a modern processorwith thousands of cores, well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks have ignited a new wave of algorithmsand tools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing, andmany others. Training deep neural networks, however, requires both highquality input data and large amounts of computation. GPUs are massivelyparallel processors capable of operating on large amounts of datasimultaneously. When combined into a multi-GPU cluster, a highthroughput pipeline may be required to feed input data from storage tothe compute engines. Deep learning is more than just constructing andtraining models. There also exists an entire data pipeline that must bedesigned for the scale, iteration, and experimentation necessary for adata science team to succeed.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

A data scientist works to improve the usefulness of the trained modelthrough a wide variety of approaches: more data, better data, smartertraining, and deeper models. In many cases, there will be teams of datascientists sharing the same datasets and working in parallel to producenew and improved training models. Often, there is a team of datascientists working within these phases concurrently on the same shareddatasets. Multiple, concurrent workloads of data processing,experimentation, and full-scale training layer the demands of multipleaccess patterns on the storage tier. In other words, storage cannot justsatisfy large file reads, but must contend with a mix of large and smallfile reads and writes. Finally, with multiple data scientists exploringdatasets and models, it may be critical to store data in its nativeformat to provide flexibility for each user to transform, clean, and usethe data in a unique way. The storage systems described above mayprovide a natural shared storage home for the dataset, with dataprotection redundancy (e.g., by using RAID-6) and the performancenecessary to be a common access point for multiple developers andmultiple experiments. Using the storage systems described above mayavoid the need to carefully copy subsets of the data for local work,saving both engineering and GPU-accelerated servers use time. Thesecopies become a constant and growing tax as the raw data set and desiredtransformations constantly update and change.

Readers will appreciate that a fundamental reason why deep learning hasseen a surge in success is the continued improvement of models withlarger data set sizes. In contrast, classical machine learningalgorithms, like logistic regression, stop improving in accuracy atsmaller data set sizes. As such, the separation of compute resources andstorage resources may also allow independent scaling of each tier,avoiding many of the complexities inherent in managing both together. Asthe data set size grows or new data sets are considered, a scale outstorage system must be able to expand easily. Similarly, if moreconcurrent training is required, additional GPUs or other computeresources can be added without concern for their internal storage.Furthermore, the storage systems described above may make building,operating, and growing an AI system easier due to the random readbandwidth provided by the storage systems, the ability to of the storagesystems to randomly read small files (50KB) high rates (meaning that noextra effort is required to aggregate individual data points to makelarger, storage-friendly files), the ability of the storage systems toscale capacity and performance as either the dataset grows or thethroughput requirements grow, the ability of the storage systems tosupport files or objects, the ability of the storage systems to tuneperformance for large or small files (i.e., no need for the user toprovision filesystems), the ability of the storage systems to supportnon-disruptive upgrades of hardware and software even during productionmodel training, and for many other reasons.

Small file performance of the storage tier may be critical as many typesof inputs, including text, audio, or images will be natively stored assmall files. If the storage tier does not handle small files well, anextra step will be required to pre-process and group samples into largerfiles. Storage, built on top of spinning disks, that relies on SSD as acaching tier, may fall short of the performance needed. Because trainingwith random input batches results in more accurate models, the entiredata set must be accessible with full performance. SSD caches onlyprovide high performance for a small subset of the data and will beineffective at hiding the latency of spinning drives.

Readers will appreciate that the storage systems described above may beconfigured to support the storage of (among of types of data)blockchains. Such blockchains may be embodied as a continuously growinglist of records, called blocks, which are linked and secured usingcryptography. Each block in a blockchain may contain a hash pointer as alink to a previous block, a timestamp, transaction data, and so on.Blockchains may be designed to be resistant to modification of the dataand can serve as an open, distributed ledger that can recordtransactions between two parties efficiently and in a verifiable andpermanent way. This makes blockchains potentially suitable for therecording of events, medical records, and other records managementactivities, such as identity management, transaction processing, andothers.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the systems described above may be bettersuited for the applications described above relative to other systemsthat may include, for example, a distributed direct-attached storage(DDAS) solution deployed in server nodes. Such DDAS solutions may bebuilt for handling large, less sequential accesses but may be less ableto handle small, random accesses. Readers will further appreciate thatthe storage systems described above may be utilized to provide aplatform for the applications described above that is preferable to theutilization of cloud-based resources as the storage systems may beincluded in an on-site or in-house infrastructure that is more secure,more locally and internally managed, more robust in feature sets andperformance, or otherwise preferable to the utilization of cloud-basedresources as part of a platform to support the applications describedabove. For example, services built on platforms such as IBM's Watson mayrequire a business enterprise to distribute individual user information,such as financial transaction information or identifiable patientrecords, to other institutions. As such, cloud-based offerings of AI asa service may be less desirable than internally managed and offered AIas a service that is supported by storage systems such as the storagesystems described above, for a wide array of technical reasons as wellas for various business reasons.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models.”

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. Likewise, machines likelocomotives and gas turbines that generate large amounts of informationthrough the use of a wide array of data-generating sensors may benefitfrom the rapid data processing capabilities of an edge solution. As anadditional example, some IoT devices such as connected video cameras maynot be well-suited for the utilization of cloud-based resources as itmay be impractical (not only from a privacy perspective, securityperspective, or a financial perspective) to send the data to the cloudsimply because of the pure volume of data that is involved. As such,many tasks that really on data processing, storage, or communicationsmay be better suited by platforms that include edge solutions such asthe storage systems described above.

Consider a specific example of inventory management in a warehouse,distribution center, or similar location. A large inventory,warehousing, shipping, order-fulfillment, manufacturing or otheroperation has a large amount of inventory on inventory shelves, and highresolution digital cameras that produce a firehose of large data. All ofthis data may be taken into an image processing system, which may reducethe amount of data to a firehose of small data. All of the small datamay be stored on-premises in storage. The on-premises storage, at theedge of the facility, may be coupled to the cloud, for external reports,real-time control and cloud storage. Inventory management may beperformed with the results of the image processing, so that inventorycan be tracked on the shelves and restocked, moved, shipped, modifiedwith new products, or discontinued/obsolescent products deleted, etc.The above scenario is a prime candidate for an embodiment of theconfigurable processing and storage systems described above. Acombination of compute-only blades and offload blades suited for theimage processing, perhaps with deep learning on offload-FPGA oroffload-custom blade (s) could take in the firehose of large data fromall of the digital cameras, and produce the firehose of small data. Allof the small data could then be stored by storage nodes, operating withstorage units in whichever combination of types of storage blades besthandles the data flow. This is an example of storage and functionacceleration and integration. Depending on external communication needswith the cloud, and external processing in the cloud, and depending onreliability of network connections and cloud resources, the system couldbe sized for storage and compute management with bursty workloads andvariable conductivity reliability. Also, depending on other inventorymanagement aspects, the system could be configured for scheduling andresource management in a hybrid edge/cloud environment.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. Big data analytics applications enable datascientists, predictive modelers, statisticians and other analyticsprofessionals to analyze growing volumes of structured transaction data,plus other forms of data that are often left untapped by conventionalbusiness intelligence (BI) and analytics programs. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form. Big data analytics is a form of advanced analytics,which involves complex applications with elements such as predictivemodels, statistical algorithms and what-if analyses powered byhigh-performance analytics systems.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

As used within the herein embodiments, some example features for storagesystem concepts are presented for storage systems, storage systemelements, different types of durable storage, storage controllers,storage devices, storage device controllers, and combinations of thesefeatures. Further, additional examples of a memory component withmultiple types of durable storage, multiple ways to address data, andmultiple ways to implement durably stored data are described inapplication Ser. No. 15/697,540, which is incorporated herein in itsentirety.

In some examples, a storage system may be considered to include acombination of hardware and software that implements capabilities forstoring and retrieving data on behalf of servers, applications,databases, or any other software and/or hardware module configured tocommunicate with the storage system. A storage system may include datastorage features and performance management capabilities. Further, astorage system may implement mechanisms that increase reliability bysupporting continued operation and a reduced probability of data loss inthe event of a variety of hardware or software component failures.

In some examples, a storage system element may be an identifiable part,component, or module within a storage system, where the part, component,or module may be implemented as a circuit board, power supply, fan,interconnect, or subcomponents thereof. In some cases, the storagesystem element may be implemented as a software module or application.

In some examples, durable storage may be a type of storage that isdesigned to retain stored content in the event of software crashes orfaults, storage system reboots, storage system power loss, or failuresof nearby, or connected, storage system elements, or some other type offault.

In some examples, a storage controller may be a part of a storage systemthat implements, or at least coordinates, advertised capabilities of astorage system. A storage system may include one or more storagecontrollers to improve reliability, to improve performance, or toimprove both reliability or performance.

In some examples, a storage device may be an element of a storage systemthat comprises physical durable storage to be presented to storagesystems users, clients, or to other parts of the storage system, inaddition to any other hardware, software, firmware, or combination ofhardware, software, or firmware in order to present usable storage.

In some examples, a storage device controller may be part of a storagedevice that controls the storage device. A storage device controller maybe implemented as a CPU, an ASIC, an FPGA, or software module thatimplements capabilities for managing durable storage and interactioncapabilities of the storage device.

In some examples, an integrated controller and storage component mayimplement functions of a storage device controller and a storage device,as described above—where the integrated controller and storage componentmay be combined into a unified storage element, such as a removablecircuit board, that implements both the storage device controllercapabilities for interacting with the storage controller and the durablestorage capabilities on other integrated controller and storagecomponents. Further, the storage device capabilities of the integratedcontroller and storage component may be presented such that they may beused from the storage device controllers on multiple integratedcontroller and storage components. In some cases, CPUs and variouscontroller chips may serve multiple, divided, or combined purposesacross the two basic functions of implementing or coordinating thestorage system capabilities versus managing the physical hardwareelements to make stored data durable.

In some examples, a single storage device may include multipleaddressable storage classes. In some implementations, storage devicesmay refer to generic SSDs, which generally emulate disk drives andusually provide a simple address range of blocks that is internallyvirtualized to map onto erase blocks dynamically, such as using a FlashTranslation Layer. In other cases—particularly in the case of fastdurable storage, mapped durable storage, or durable registers—storagedevices are presumed to include one or more of the following components:addressable fast durable storage, addressable bulk durable solid statestorage, and a storage device controller.

In some examples, addressable fast durable storage may be storage withhigh bandwidth and low latency that supports a high number and rate ofoverwrites. Fast durable storage may be addressed with PCI transactions,for example NVMe, with direct memory addressing by a CPU on a separatecomputer system, for example, a separate storage controller, or withsome other communication channel or protocol. Addressable fast durablestorage may be a logical construct that makes use of hardware thatserves multiple functions. Fast durable storage may be implemented inmultiple ways, including: persistent high-speed memory, for example, 3DXpoint; volatile RAM coupled to a storage device controller and abattery, capacitor, or generally an energy source with sufficient energyto transfer data stored in RAM to a different nonvolatile storage—suchas reserved bulk solid state storage—in cases of loss of external poweror loss of a primary source of power. In other words, to be durable,transferred data from RAM into the other nonvolatile storage isidentifiable and retrievable at some time subsequent to loss of power toRAM, or during recovery from some other type of fault. In some cases,other types of fast durable storage memory types may be used, such aslow capacity enterprise single-level cell (SLC) Flash memory, where thefast durable storage is designed for high bandwidth, high overwrites,higher lifespans—which may result in the fast durable storage having ahigher price, or lower density per bit than other types of solid statedurable storage that may be used for long term storage.

In some examples, addressable bulk durable solid state storage may bedesigned for lower cost and higher density, where the lower cost andhigher density may be in exchange for higher write or access latency andreduced lifespan in the face of high overwrite rates. One example isflash memory, and in particular, multi-level cell (MLC), triple-levelcell, or quad-level cell (QLC) flash memory that stores two, three, orfour bits per flash memory cell at the expense of reduced bit-levelreliability, increased write latency, more disturbance of nearby readoperations, or reduced lifespans in the face of flash erase block reuse.An encompassing storage device may use various techniques to optimizeperformance or reliability of this type of bulk storage, includinginternal use of fast storage as a frontend to respond more quickly or toallow multiple operations to be organized more effectively or toimplement internal atomic operations related to bulk memory operations.

In some examples, a storage device controller may be configured toperform one or more of: receiving processing requests to store orretrieve data from addresses associated with fast durable storage on thestorage device; receiving and processing requests to store or retrievedata from addresses associated with bulk durable storage on a storagedevice; receiving and processing requests to transfer data from fastdurable storage on a storage device to bulk durable storage on thestorage device; or in response to a power failure, transfer content fromvolatile memory that is part of an implementation of fast durablestorage to bulk durable storage using stored energy from a battery,capacitor, or some other energy storage device. Further, a storagedevice controller may use CPUs associated with general storage devicecontroller functions or may use dedicated or secondary functionlow-power CPUs, or this may be a feature built into an FPGA or ASIC.

A storage device may further support one or more of the followingfeatures, with corresponding implementations to: (a) map a region offast durable storage as I/O memory or virtual memory to one or more CPUcores or I/O memory controllers on a storage controller, such that CPUstore operations or DMA transfers to I/O memory or virtual memory can bepersisted in case of power failure, where this I/O memory or virtualmemory can be written to directly from a storage controller's CPUinstructions with few operations system scheduling delays; (b) receiverequests to store an integer value (e.g., an 8 byte, 16 byte, or 32 bytevalue) into one of an index of memory locations, where the memorylocations may be referred to as a type of register, where the registersare durable in case of power failure, where durability may beimplemented by writing into a dedicated region of durable fast storage;these received requests may be piggybacked with other requests, such asto be applied before or after or at the point in time the other requestis processed to the point of being durable; (c) operate using the NVMeStorage Performance Development Kit API, or some alternative API thatsupports high-speed storage operations without the need for completioninterrupts; such an API may exclusively support fast durable storage, ormay be supported for both fast and bulk durable storage on the storagedevice—or the storage device may internally utilize some of the faststorage at its front-end, where such operations may be addressed toaddressable bulk storage; (d) receive requests to transfer contents offast durable storage or bulk solid state storage to the fast durablestorage or bulk solid state storage within the storage device, or insome cases, to the fast or bulk storage of another storage device withina storage system that included multiple storage devices; (e) providemultiple interconnect interfaces, such as based on dual NVMe, SCSI, orEthernet interfaces, to improve reliability in cases of internal storagesystem interconnect faults, or to improve performance by providingmultiple pathways within the storage system, or allowing dedicatedpathways to each of, for example, one or more storage controllers; and(f) provide directly addressable pages and erase blocks to storagecontrollers for a set of flash memory chips that include the storagedevice's bulk durable storage, thus allowing the storage controllers tohandle wear leveling, failed erase block management, and other aspectsof managing flash memory life cycles that are otherwise often handled bya controller chip paired with flash memory chips on storage devicesthemselves.

In some implementations, a storage system may be built from a collectionof storage devices, including some number of storage controllers thatimplement or coordinate the features and logic for the storage systemitself. An example of such a storage system is depicted within thestorage system architecture of FIG. 1D. Further, storage controllerlogic may implement logic to create a reliable storage service that maysurvive failures such as faults of storage devices, individual elementsof storage devices, individual storage controllers, storage controller,or storage device logic, power supplies, or internal networks—all whilecontinuing to operate and provide storage system services. Such storagesystem services may include implementing a SCSI storage array, an NFS orSMB based file server, an object server, a database service, a servicefor implementing extensible storage related applications, orcombinations of such services. Storage operations may include creatingsnapshots, replication, online addition or removal of storage devices ortrays of storage devices or storage controllers or power supplies ornetworking interfaces, or administrative operations such as creating,modifying, or removing logical elements such as volumes, snapshots,replication relationships, file systems, object stores, application orclient system relationships, among other operations.

In some implementations, storage devices may store and retrieve data onbehalf of—or coordinated through—storage controllers, largely at thedirection of storage controllers, and are generally otherwise relativelypassive participants in contributing to the overall implementation of astorage service. However, in some examples, storage device may beadditionally configured to assist, or offload, storage controllerfunctions in order for the storage controller to provide more efficientservices, such as by a storage device partially implementing garbagecollection, data scanning and scrubbing, or other such services, oraiding in bootstrapping the storage controllers.

For further explanation, FIG. 3C sets forth a diagram illustrating anexample integrated storage controller and device elements thatintegrates accessible fast bulk storage into a bulk storage device,referred to here as a unified storage element (320), where a storagesystem (124) may be implemented using multiple unified storage elements(320). Although depicted in less detail, the unified storage element(320) depicted in FIG. 3C may be similar to the storage systemsdescribed above with reference to FIGS. 1A-1D and FIGS. 2A-2G, as theunified storage element (320) may include some or all of the componentsdescribed above.

In the example architecture depicted in FIG. 3C, multiple storagecontrollers (125) and multiple storage devices are integrated into aunified storage element (320), where the unified storage element (320)may be removable as a whole or that may be on a single circuit board, orthat may be controller by a common CPU or controlled by multiple CPUs,FPGAs, or ASICs—where each such unified storage element (320) within astorage system serves as both storage controller and storage devicefunctions. In such a storage system, the storage controller function ofa unified storage element (320) may access the storage device functionon a plurality of unified storage elements, configured similarly tounified storage element (320), within the storage system. In thisexample, the storage controller function might be able to access thestorage device function on other unified storage elements within thestorage system, such as through internal networks within the storagesystem, or the storage controller function on a first unified storageelement might have to go through the storage controller function on asecond unified storage element to get the unified storage elementstorage device function.

In some examples, a reference to a storage device may refer to either aseparate storage device within a storage system or to the storage devicefunction within a unified storage element (320) within a storage system.Further, in other examples, a reference to a storage controller mayrefer to either a separate storage controller within a storage system orto the storage controller function within a unified storage element(320) within a storage system.

In another implementation, a storage system may include variouscombinations of elements comprising dedicated storage controllerswithout integrated storage device capabilities, storage devices withoutintegrated storage controller capabilities, or combined storagecontroller and device elements. Such combinations may be useful formigrating a storage system from one generation that operates in one wayto another generation that operates in a different way, or aspects ofscale may dictate some extra numbers of one function versus anotherfunctions—for example, a bulk archive storage device including a largenumber of extended storage devices in a system whose core is built fromcombined storage controller and device elements, or a performanceoriented device that needs performance or external interconnects mightbenefit from additional storage controllers, but without benefittingfrom additional durable capacity.

In some examples, a storage system may be configured to be reliableagainst complete failure of one or two or more storage devices usingerasure codes, such as erasure codes based on single or double parity,as with RAID-5 or RAID-6 , or against uncorrectable faults, corruptions,or complete failures of individual elements within a storage device—suchas individual pages or erase blocks in flash memory or of individualchips—by reconstructing content from elsewhere within the same storagedevice or through the single and double parity protection used forcomplete device failure. In other words, in some examples, the variousmemory components of a unified storage element (320), individually or incombination, may be used to implement multiple, different RAID levels orcombinations of RAID levels. In the following examples, a RAID stripe isdata that is stored among a set of memory regions mapped across a set ofstorage devices, where each memory region on a given storage devicestores a portion of the RAID stripe and may be referred to as a “strip,”a “stripe element,” or a “shard.” Given that the storage system (306)may simultaneously implement various combinations of RAID levels, a“RAID stripe” may refer to all the data that is stored within a givenRAID stripe corresponding to a given RAID level. Generally, in thefollowing examples, a stripe, stripe element, or shard is one or moreconsecutive blocks of memory on a single solid state drive—in otherwords, an individual stripe, stripe element, or shard is a portion of aRAID stripe distributed onto a single storage device among a set ofstorage devices. In this way, a RAID level may depend on how the RAIDstripe is distributed among a set of storage devices. With regard toerasure coding, generally, erasure coding may be described in terms ofN+R schemes, where for every N units of content data, an additional Runits of redundancy data is written such that up to R failures may betolerated while being able to recover all N units of content data. Forexample, RAID-5 is an example N+1 scheme, whereas RAID-6 describes a setof N+2 schemes. In other examples, a scheme may be based on GaloisFields, or other types of mathematics that can cover a wide range of Nand R combinations. Specifically, in some cases, each given storagedevice within a storage system may, by default, detect faults locallyand directly using, as some examples, a checksum or mathematicalintegrity check for all data that is read or written to the givenstorage device. However, in cases where a given storage device in astorage system does not perform local fault detection, other techniquesmay be applied, such as various coding techniques that store additionalshards beyond a fault tolerance (e.g., three parity shards that areusable to recover up to two faults and up to one unit of additionalcorrupted data), or through the use of encoding all data, includingparity or redundancy data, in a manner that can be used to detectcorrupted data. In general, in the embodiments described herein, a localstorage device will, by default, perform localized data integritychecks, where data shards and redundancy shards may be distinct fromeach other. However, in some examples, no such restriction to, orreliance upon, localized integrity checking is presumed.

It should further be noted that erasure coding schemes may be usedwithin a storage system in a variety of ways. Traditional storagesystems, which were originally designed for spinning disk storagedevices, typically allocate large RAID-5 and RAID-6 layouts relativelystatically as a set of relatively large, such as multi-gigabyte, N+Rshards, where each shard is a unit of stored content data or a unit ofstored redundancy data. In such schemes, overwrites of previously storedcontent includes replacing existing content data shards and calculatingand replacing existing redundancy data shards using some sequence ofsteps to ensure that the operations may be done safely in the event thata fault occurs during the sequence of steps. In some cases, thesesequence of steps may be computationally intensive, for example, in somecases, the sequence of steps includes reading old content and redundancydata, or reading from most existing content data shards at the samelogical offset in a RAID layout in order to calculate new redundancydata. In other examples, the sequence of steps may include multiplepersisting steps in order to ensure that an interrupted operation can berecovered when safe operation resumes.

In examples using spinning disk storage, faults may include corruptedblocks, which can generally be corrected by overwriting them to correcta temporary coding error on the spinning disk, or by allowing thespinning disk to revector a bad block to an alternate location. Spinningdisks may also suffer from electrical or mechanical failure fromproblems driving their electric motor, seizing of the electric motor, orfailure of a read/write head or actuator. In some cases, sudden andexcessive vibration can cause a read/write head to crash, or physicallymake contact, with a spinning disk surface, thereby damaging the head orscratching the disk surface, or scattering particles within ahermetically sealed device. In other cases, electronics can fail or thehermetic seal may be damaged, thereby exposing the internal componentsto an uncontrolled environment with moisture or dust particles that maylead to disk failure. In short, while spinning disks may generally bereliable, there are a great many ways in which a spinning disk mayencounter a catastrophic failure; however, there are exceptions wherefailures may be fixed with internal recoveries and rewrites for theoccasional bad sector or bad track, or runs of a few bad sectors or badtracks.

By contrast to spinning disks, solid state storage does not suffer frommechanical failures, and performance is not affected by vibration, andit is unlikely that a solid state storage device causes voltage spikesor other electrical issues. Further a solid state storage device is notgenerally a single device, but rather a collection of individualintegrated circuits, or a collection of regions within a particularintegrated circuit that may age and fail separately, such as due to aflash cell's limited number of erase/recycle cycles or due tochip-to-chip or cell-block-to-cell-block manufacturing variations.However, performance of some solid state storage device technologies, inparticular flash based storage devices, can be greatly affected byinternal operations related to erase block writing, erasing, or othermanagement operations or—particularly for TLC and QLC flashdevices—reads performed on nearby cells. Individual elements withinsolid state devices that do their own garbage collection can become slowor unresponsive for periods of time when garbage collection is beingperformed. Flash, in particular, can have much higher rates ofindividual damaged blocks, or of unusable blocks, than disk, and mayrequire more internal error correction and more spare blocks. Given theway flash memory works, advertising a much lower available capacity thanthe raw physical capacity may be used to extend the lifespan of flashdrives by reducing the frequency of erase cycles that result from randomoverwrites and delaying garbage collection. Such lifespan extensiontechniques may be coupled with algorithms to randomize the locations ofwritten data, perhaps with biases against or in favor of blocks thathave been erased more times than other blocks in dependence upon anexpected longevity of the stored data.

Generally, flash does not suffer from slowdowns due to randomization ofdata locations because given that there is no mechanical delay inreading a block, any read unaffected by a concurrent write or aconcurrent garbage collection is generally quite fast irrespective ofwhether previous, subsequent, or concurrent reads are from nearby orfarther away locations or logical addresses. In some cases, a penaltyfor turning relatively sequential reads into relative random reads is sohigh for disk, that disk based storage systems generally avoidrandomizing locations of data so that logical sequential reads byapplications stay sequential on disk, which is part of why it is socommon for RAID-5 and RAID-6 mappings to be kept relatively static, withthe mapping retained when data is overwritten (some files system basedmodels such as ZFS are exceptions to this). In short, in the case ofspinning disks, small overwrites to RAID-5 and RAID-6 datasets oftenrequire a read-old-data, read-old-parity, write-new-data,write-new-parity sequence that can be quite expensive. Simpler mappingsused for disk-based storage systems also reduce the amount of index datathat has to be kept and managed in order to determine where a block isstored. In particular, if a random read requires reading both an indexblock—to figure out where a block is stored—and the block itself, thenthat includes two random reads, and two mechanical delays, which is evenworse from a performance perspective because latency may double andthroughput may be reduced due to limited available TOPS.

Further, because solid state storage does not have mechanical delaysinherent in spinning disks, an occasional or even frequent extra read tofigure out where a block resides does not incur a high performancepenalty, consequently, scattering data as it is written (resulting in alarge index that can exceed available cache memory that results in theneed to read the index in order to locate data) has few downsides. Forexample, performance impact for a small read performed before a largeread is performed will be negligible. Further, bandwidth for servicing alarge sequential read request by issuing a set of smaller physicalrandom reads (the result of scattering content on writes and overwrites)is generally only a bit less than the bandwidth for servicing largesequential read requests by issuing a small number of larger physicalreads. By contrast to solid state storage, with spinning disks, TOPSlimits can result in throughput hits from randomized data locations thatcan be multiple orders of magnitude if stored logically sequential datais physically highly fragmented.

Consequently, because of the different characteristics of spinning disksand solid state storage, solid state storage solutions generally avoidoverwriting stored data by writing to new locations and onto fresherasure coded stripes as data is persisted. Data may be written into Ndynamically allocated shards of a few kilobytes to a few megabytes ascontent data is written, with R matching redundancy shards allocated andwritten to match, thereby achieving N+R recoverability. Such a protocolfor processing writes avoids replacing existing data in place, thusavoiding the need to modify in-place redundancy data in place throughsome safe update scheme. Collections of stripes may then be written outbased on whatever patterns achieve the best write throughput, wherethroughput of later read requests depends on the high TOPS rates ofsolid state storage that is often limited by available throughput of I/Ointerconnects instead of limited by delays in switching betweensectors—at least as long as the storage devices are properly optimizedand include a sufficient number of flash chips.

In some implementations of a unified storage element (320), erasurecodes may be used to ensure that any completely written data can surviveone or more device failures, or a combination of a device failure and alocalized data corruption or segment failure. Further, reliability maybe increased further by adding erasure codes within a storage device,such as by calculating erasure codes that operate across pages, eraseblocks, or chips within an individual storage device. With this furtherreliability, rare cases of an isolated, corrupted or unreadable, block,erase block, or chip within a storage device may be recovered internallywithout relying on additional storage devices. This further reliabilityis helpful in cases where undetected, and thus uncorrected, isolatederrors exist for an extended period of time, consequently exposing thestorage system to data loss in cases where two other storage devicesfail entirely or are removed from the storage system due to accident oras part of a procedure to replace older drives within newer ones, andthe latent isolated corrupted blocks cannot then be corrected by an N+Rredundancy technique. In dependence upon uncorrected errors within adevice being rare or having a low probability of occurring, redundancydata across pages, erase blocks, or chips may be written in either alazy fashion or as data ages. In this example, these lazy writes mayresult in some lost intra-device redundancy in cases of power failure,but there may be a very low likelihood that this small amount of lostintra-device redundancy may be needed to recover on restart from a rarelocalized uncorrectable error coupled with a simultaneous failure for Rdevices to startup. Bandwidth for writing data in such a storage systemmay often be improved by writing several stripes at once. Writingseveral stripes at one may be done by keeping multiple stripes open forfurther updates to allow use of all storage devices under heavy loadsand to allow activity to be balanced across large numbers of eraseblocks (in the case of flash) or large numbers of solid state storagechips. Keeping multiple stripes open to maximize parallelism acrosserase blocks, chips, or storage devices may depend upon custom storagedevices and networks or buses that support large numbers of paralleltransfers. Bandwidth for reading data generally depends upon support fora large number of parallel transfers between storage controllers andstorage devices. Getting the best read throughput may also need toaccount for, or schedule around, particular flows of writes to storagedevices, storage device and internal network channels, solid statechips, or flash erase blocks in order to reduce contention withwrite-related operations. Reads that may be slowed down by writecontention, or that may severely slow down write operations themselves(as is common in flash devices) might be delayed, might delay writeoperations, or might look for alternate means of reading data, such asrecovering data from erasure codes stored within or across storagedevices.

In some cases, stored data may be stale or no longer useful whilecontinuing to occupy storage space, such as, for example, data that hasbeen written and scattered, and overwrites that are separately scatteredfrom the data they overwrite, and deletion of data through deletingsnapshots or volumes or through UNMAP or TRIM requests, or through SCSIExtended Copy or similar types of requests. Generally, some garbagecollection process may identify pages or segments or erase blocks thatare no longer used, or that are no longer used completely, and movewhatever useful data remains in a segment to another memory location,and then allowing pages or segments or erase blocks to be used for newstored data. In the case of flash memory, erase blocks may be garbagecollected and erased before they can be reused for new data.

In some implementations, further optimizations may be based on a storagemodel, such as staging speculative writes through fast memory of aunified storage element (320). For example, in some storage systems,there may be a write into some kind of fast storage to record anoperation, or record operation results, in a way that makes theoperation or the results persistent more quickly—thus enabling one ormore of the following benefits, including: (a) enabling operations to besignaled as completed faster; (b) enabling internal storage systemprocesses to be unblocked more quickly while allowing a larger queue ofchanges to be built up for writing to slower bulk storage; or to reducethe likelihood of temporary data, which may be overwritten quickly, frombeing written to bulk storage in the first place, thereby improvingflash lifespans; or (c) enabling multiple operations to be mergedtogether for more efficient or better organized storage operations,thereby improving flash lifespan as well as potentially increasingthroughput. Further, if individual storage devices in a storage systemprovide both fast durable storage (such as 3D Xpoint memory or DRAMbacked by a battery or capacitor which is written to flash on powerfailure) and bulk storage (e.g., bulk MLC, TLC, or higher bit-per-cellFlash), then the typical means of staging through fast storage may beimproved. In a typical storage system implementation, a storage systemwrites to fast storage, for example, by writing at least two copies intoat least two separate fast memories each on separate storage devices,and, sometime later, the storage system writes that data to the bulkstorage. Such a write operation implementation may result in two sets oftransfers from storage controllers to various storage devices, one setof transfers to fast storage, another set of transfers to bulk storage.These two sets of transfers use considerable extra bandwidth betweenstorage controllers and storage devices because the same data istransferred at least twice, and possibly more than twice. For example,staging a content data write by writing three copies to fast storage onthree storage devices followed by grouping of data together into an N+2erasure coded stripe may result in total bandwidth that is over fourtimes the size of the write itself. This consumption of bandwidth may bereduced by using an M+2 encoding for storing into the fast durablestaging space—where M is a separate number of content data shards, whichare then coupled with redundancy data shards for writing to fast stagingspace; however, there may still be bandwidth usage that is over twotimes the size of the write itself

One example for reducing this bandwidth overhead is for the storagedevice to use the fast storage as internal staging space, acknowledgingthe write more quickly to storage controllers, and then writing to bulkstorage at a later point in time—where the later write to bulk storagefrom fast storage may be invisible to the storage controllers because tothe storage controller, the write was persisted, or durably stored atthe moment that the write was acknowledge. In some cases, such a writeprotocol—where a storage controller is unaware of internal transfers ofdata—may eliminate the need for separately addressable fast durablestorage. However, there is significant flexibility to be gained byallowing the storage controllers to manage the process of transferringdata between fast storage and bulk storage, rather than having thestorage devices do these transfers implicitly behind the scenes andhiding the fast durable storage from the storage controllers. In otherwords, storage system implementations gain a lot of flexibility foroptimizing their overall operations by allowing higher level aspects ofthe implementation to record to fast persistent storage early in aprocessing pipeline, and losing that flexibility to gain reduced backendbandwidth may be an unacceptable tradeoff.

In some embodiments, a solution to reduce bandwidth for backend datatransfers is to utilize staging a write through fast storage. In thisexample, if the write is first written as three copies to fast storageon the respective fast storage of three separate storage devices, wherethe contents of that multi-copy write are aggregated with the contentsof other writes to form an N+2 protected stripe in bulk storage, then ifone of the storage devices selected serving as fast storage is the samestorage device to be selected for the eventual write to bulk storage,then the extra storage-controller-to-storage-device bandwidth fortransfer to the bulk storage may be avoided by the storage controllerinstructing the storage device to transfer that data from fast storageto bulk storage. In some cases, avoiding extra transfers may be achievedby transformations such as merging. In this example, the other twostorage devices that stored a respective copy in fast storage retaintheir respective copy until the final N+2 stripe has been written andcommitted, but otherwise outside of a fault and recovery sequence, theother two storage devices do not need to perform additional,corresponding, write or copies of the respective copies. In someexamples, the format of data written to fast storage is identical to theformat of data written to bulk storage, and consequently, no datatransformation is needed prior to a transfer. In other cases, where theformat of data written to fast storage is not identical to the format ofdata written to bulk storage, the transfer may include transforming thecontent during the transfer from fast storage to bulk storage, where thetransformation may be based on instructions from a storage controller,and possibly in coordination with merged content from other storedwrites in fast durable storage.

In some examples, transfers to fast storage from bulk storage or fromfast storage to bulk storage may operate simultaneously, where suchparallelism may increase bandwidth or reduce locking contention issueswithin a storage controller software implementation. For example,separate CPU cores in a storage controller may use separate channels tostorage devices, thereby eliminating locking contention while gainingbandwidth for every storage controller CPU core that transfers tostorage devices.

Further, separation of transfers into fast storage from the transfer ofcontent from fast storage to bulk storage allows further types ofoptimizations. For example, data may be written as M+R₁ stripes in fastbulk storage to be stored in an N+R₂ stripe in bulk storage, or data maypartially or completely fill a subset of shards within an N+R striperesulting in intermediate M+R stripes where M<N. In some example, R, R₁,and R₂ may be equal, but in other examples, may be defined to bedistinct values. Further, as an example, within a 10+2 stripe, there maybe enough accumulated data to write partial segments as a 5+2 stripe,thereby allowing fast write data to be written with reliability againsttwo failures, but with only 40% additional overhead rather than 200%additional overhead that would be required for writing 3 completecopies. In this example, after all of the N+2 shards are complete, thecorrected P and Q parity (given a RAID-6 erasure code format) may bewritten to storage devices, and the storage devices with fast durablestorage that includes the completed data shards may be instructed towrite the completed data shards from fast storage to bulk storage.

In this example, the overhead of a write of three independent copies tofast storage followed by an optimized N+R transfer to bulk storage mayincur bandwidth consumption such that slightly more than three times asmuch data to be transferred from storage controllers to storagedevices—compared with four times as much without the instruction totransfer from fast storage to bulk storage within a storage device.However, the previous example may reduce the overage transfers inbetween fast storage and bulk storage to 160% of the total data—and inexamples with wider stripes, the overhead could be reduced furtherstill.

In some examples, multiple M+R partial stripes may be writtenconcurrently into fast storage, including for the same N+R stripes orfor different stripes. In this example, each M+R stripe may be protectedby its own partial or complete parity shards. The partial or completeparity shards should be identifiable in the written data, such as byrecording the M+2 layout in the content written out to each section offast storage, or by recording a sequence number or other identifier thatcan be compared across storage devices during some later recovery.

In some embodiments, a variation may be to interleave small writesacross partial sections of most or all shards of an N+R stripe,calculating, say, P and Q one section at a time. When the completecontents for an N+R stripe are available, a storage controller mayinstruct the storage devices to transfer now complete shards torespective bulk storage. In some cases, the format of the partialstripes may include headers or other information that differs slightlyfrom the format that will be written to bulk storage; if so, the storagedevice may be instructed to transform the format and to calculateupdated redundancy shards.

Further, if data is accumulated in fast storage for some period of timebefore being transferred to bulk storage, then the storage controllersmay determine that some of the content is no longer needed, or will notbe requested, and in response this determination, the storagecontrollers may partially or completely avoid a transfer to bulkstorage. Avoiding such transfers may occur based on the determinationthat data has been overwritten, or if the data is metadata that has beenreorganized or optimized. Avoiding such transfer may also happen incases where deduplication operations include determining that some datahas already been written to the bulk storage somewhere in the storagesystem, thereby avoiding the transfer of data that already exists. Inother examples, partially replaced data, such as in the case of M+Rstripes or partially written shards in an N+R stripe might becomeinvalid or unnecessary before being transferred to bulk storage, and asa result, parts of the final N+R stripe may be replaced, eliminated, orreorganized prior to any such transfer.

In different embodiments, because fast storage space may be limited, thestorage system implementation ensures that data that is to ultimately bewritten to bulk storage is actually written to bulk storage and madereliable across storage devices in a sufficiently timely manner that thestorage system does not normally have to pause waiting for fast storageto be freed up for reuse.

In some implementations, consistent recovery may be performed withvarying subsets of storage devices. For example, if a storage controllerfails and reboots, or one controller fails and another controller takesover for the failed controller, or when some number of storage devicesgo offline and then come back online, or when a storage system isstopped through some sequence of events (power failure, power surge, aninadvertent administrative shutdown command, or a bug, among otherexamples), the storage service may have to be restarted and recoveredfrom the available storage devices. During that recovery, there may be asecond event that results in the storage service going offline and thenlater being recovered, and that sequence of interrupted recoveries mayrepeat itself many times. During each recovery, a different set ofstorage devices may come online, or some storage devices may come onlinemuch slower than other storage devices, resulting in the storage servicedeciding to recover from the online storage devices, but with adifferent set of such storage devices each time. Such a sequence ofinterrupted recovering may present an issue for data that is storedredundantly across storage devices, such as through writing the samedata to multiple such storage devices (RAID-1 or mirroring) or throughwriting an erasure code (such as RAID-5 or RAID-6) across multiplestorage devices intended to achieve reliability in the face of one ormore storage device failures.

As one example of handling interrupted recoveries, consider storagedevices utilized in a storage system that is two-drive-failureredundant, such as one that writes N+2 segments of data as RAID-6 stylestripes formed as some number, say N, units of data (e.g., shards) andtwo similarly sized shards storing P and Q parity. Consider further thatsegments are written out as new data flows into a storage system, withno fixed correspondence of the virtual address of stored data to thephysical location of that data, rather than being partially rewritten inplace to fixed locations as has been typical of traditional RAID storagesystems.

In this example, the content of a storage system may be considered to becommitted and recoverable content may be determined after a restart thatonly had access to the persisted content. However, there may be aproblem: the recoverable content depends on the specific set of storagedevices that are available and ready at the time recovery starts. If Nor N+1 drives out of N+2 drives were written for a particular N+2 stripeprior to the fault that preceded a recovery, then recoverability dependson which drives are available when recovery starts. If N of the drivesthat were written are available on recovery, then the content of thatRAID stripe is recoverable. If N−1 of the drives that were written areavailable for recovery, then at least some data from that stripe is notrecoverable, yet one or two drives that were not written for somestripes might be available during startup, so there are combinations ofone or two drive failures that can lose some partially written data andother combinations of one or two drive failures that do not lose thesame partially written data.

Continuing with this example, one solution for this problem is tooperate at startup, based on whatever data is recoverable, and to ignoreany data that is not recoverable. Under normal operation, an incompletewrite of all shards of an N+2 stripe should only happen if somein-progress writes did not complete prior to a storage system or storagecontroller fault, or if a storage device fails resulting in a persistentpartial failure. In this way, a storage device failure may be handled bywriting the failed shards to new locations prior to considering thewrite complete, or by somehow persistently marking the device as havingfailed so that it will not be considered as a viable source forup-to-date content on a storage system or storage controller startup.

To conclude with this example, as soon as a write of a stripe iscomplete (or as soon as any data is N+2 redundant through one or moretechniques), the storage system logic may consider the write to bedurable and recoverable and therefore the storage system may move on tosome new set of writes that depend on that previous write beingguaranteed recoverable. For example, a first write might store data thatrepresents the establishment of a snapshot of a volume, and a secondwrite might represent new data written to that volume after thesnapshot. The second write is written within the context of structuresestablished by the first write.

In some embodiments, as an alternative, the snapshot and the write maybe concurrent such that the write is included in the snapshot (thusbeing associated with one version of the durable structures for thevolume and its snapshot), or the write could be excluded from thesnapshot (thus being associated with another version of the durablestructures for the volume and its snapshots), or either the snapshot orthe write or both might be backed out as never having happened, whichare all consistent and acceptable outcomes if neither the snapshotrequest nor the write were completed and signaled to a requester ascompleted.

Continuing with this example, while the snapshot and the write are beingprocessed, the storage system or a controller might fault a first time,and the snapshot—at the time of the first fault—may have been written toN out of N+2 devices, while the write might have been written to M outof M+2 devices. In this example, during recovery, the storage system maytake action to clean up or move forward data structures so that thestorage system is ready to process new writes. Further, if one or twodevices are not available during recovery, then either the snapshot, orthe write, or both, or neither might be recoverable without the twounavailable devices. Subsequent first recovery actions may depend onwhich devices are not available, which may involve writing down new datathat is protected by some alternate L+2 data protection scheme on someother set of devices to finalize a clean structure for the volume andits chain of snapshots and of the data that does or does not fall intoone or another of the snapshots. In this example, if the writing of theL+2 redundant data is written to only L devices before a second storagesystem or storage controller fault, then subsequent second recovery ofthe first recover actions may also depend on which storage devices areavailable during the second recovery. However, a different two devicesmay be unavailable during this second recovery, thereby resulting in adifferent answer for whether the snapshot or the write are recoverableand some independent answer for whether the first recovery action isrecoverable. Such a scenario may cause a problem if the first recoveryactions determine one answer for whether a snapshot or write arerecoverable but the second recovery determines a different answer forwhether the snapshot or the write is recoverable. Note that determiningthat written information is recoverable can be calculated from theavailable stored data, whereas unrecoverable information may simply beunknown to recovery so that recovery may not explicitly determine thatit is unrecoverable. As an example, for a 2+2 RAID-6 stripe, say thatonly two data shards were written prior to a fault that resulted in alater recovery, then, if only those two shards are available duringrecovery then the data is recoverable. Otherwise, in this example, ifonly the P and Q shards are available during recovery, then there is noavailable knowledge of the stripe at all.

In this example, there may exist the following scenario: the firstrecovery determines that both the snapshot and the write into the volumeare recoverable, but the recovery action must then determine whether thewrite should be included in the snapshot or excluded from the snapshot.The first recovery actions might then include writing a record that thesnapshot exists and has a name and might include metadata changes thatinclude the write content in the snapshot, with a reference to the writecontent added to a table.

Continuing with this scenario, if during the second recovery, the writeof the L+2 data from the first recovery actions is recovered, as is thesnapshot, but the write included in the snapshot is not itselfrecovered, then there may be metadata associated with the snapshot thatincludes data which is not itself recoverable (and may be entirelyunknown given the set of devices available). Such a result may beinconsistent and may corrupt metadata, depending on how the storagesystem implementations handles this scenario.

Further, during a second recovery, actions may be taken to completerecovered actions by writing further data and metadata based on what wasrecovered, and a fault might occur during that recovery, resulting in athird recovery based on perhaps a different set of available andunavailable devices.

While it may be that the likelihood of some of these scenarios is lowbecause such scenarios may depend on particular combinations of faultsduring narrow time windows during the writing of redundant data afterjust enough data has been written for the data to be recoverable withone or two devices unavailable during recovery—without more data havingbeen written so the data is guaranteed to be recoverable no matter whichtwo devices are subsequently unavailable. In other words, such scenariosmay require a sequence of faults and recoveries where each fault happensduring one of these narrow time windows, and they require that adifferent set of one or two devices be unavailable during subsequentrecoveries. However, while these scenarios may only occur infrequently,or rarely, these scenarios are possible, and implementations should havestrategies to mitigate them.

In some implementations, strategies to prevent such scenarios mayinclude using two-phase commit, where data is written out as a set ofpending changes, and once all changes are written, a much smaller commitis written out. However, the commit may have the same problem. In otherwords, if a storage system is expected to recover with two failedstorage devices, then at least three copies of the commit must bewritten, or it must itself be written using an N+2 redundancy scheme. Asan example, if one or two of three copies are written, then one recoverysequence may be aware of the commit, while a second recovery sequencemay fail to be aware the commit, or vice versa. In this example, if theexistence of the commit itself is used as a basis for subsequent stepsin recovery, and if a second recovery depends for correctness on seeingthe same commit (or lack thereof) to ensure that anything written insubsequent steps is handled correctly, then the same issue applies whereinconsistency between a first recovery and a second recovery can lead tocorruption.

Further, the examples herein may implement any N+R scheme, where R is anumber of shards representing redundancy data, where any N valid,written, and available shards (whether data or redundancy shards) may beused to recover content. Further, if at least N shards, but fewer thanN+R shards, are written prior to a fault, then if at least N of thewritten shards are available during recovery, then the associated datais recoverable. However, if fewer than N of the written shards areavailable during recovery, then some of the content may not berecoverable. Further in this example, if there is a second fault duringa first recovery that leads to a second recovery, or an eventual thirdfault leading to a third recovery, and so on, then a different set ofavailable devices may, as described above, alter the set of writtenstripes for which a sufficient number of shards are available.

In some implementations of a unified storage element (320), recovery maybe based on recording available storage device lists. Such availablestorage device lists may provide a solution for determining a consistentset of recoverable data across multiple reboots, where the solution mayinclude terminating efforts for a second recovery that detects failedstorage devices if that second recovery follows a first recovery with anincompatible, or different, set of detected failed storage devices. Sucha recovery protocol may operate in one of two ways: during recovery,before making any other changes that could lead to inconsistencies insubsequent recoveries, an available storage device list is generatedthat indicates a set of devices that are included in the given recoverysequence. The available storage device list may be written to allavailable storage devices. Until the available storage device list iswritten to all available storage devices, and until the writes aresuccessful to a sufficient number of those storage devices that asubsequent detection on recovery is guaranteed, then further recovery isprevented from proceeding.

However, this solution may present a problem: how can a list ofavailable devices be recorded in a way that is reliably recoverable on asecond, third, or fourth recovery each with an inconsistent set ofavailable devices? While maintaining such a list may be difficult, thisis simpler information than a set of all erasure coded stripes. Oneexample solution, in a storage system that is redundant against Rfailures, includes writing the list of available devices to at least2×R+1 storage devices before proceeding further with recovery. In thisexample, with an R of two (2), a subsequent recovery that is missing adifferent set of two storage devices will see at least three of thoselists of available storage devices. Alternately, if the writing of thelist of available storage devices had been written to R storage devicesor less, a subsequent recovery might not see the list of availablestorage devices, or might see only one copy of the list. If a subsequentrecovery does not see the list of available storage devices at all, thenthe previous recovery could not have advanced to making any incompatiblechanges, and a new list of available storage devices can be expressed toa different set of 2×R+1 storage devices. If a subsequent recovery doesnot see at least R+1 copies of a list of available storage devices, aprior recovery could not have advanced to the point of making changesand the current list of available devices could be written out.Alternately, if a subsequent recovery sees any copy of the prior list ofavailable storage devices, it could use that list of available storagedevices.

Continuing with this solution, regardless of the manner in which thelist of available storage devices is made reliable, once a fault duringa first recovery has led to analysis for a second recovery, if it isdetermined that the available storage devices during analysis for thesecond recovery does not sufficiently overlap with the available storagedevices during a first recovery that might have proceeded past the pointof writing the list of available storage devices, then recovery isstopped. If a sufficient number of those storage devices do come backonline, then the second recovery can proceed after that point, but notbefore. In other words, in this example, the union of the list ofavailable storage devices from a first recovery and the list ofavailable storage devices from a second recovery cannot be a set largerthan R devices.

In some implementations of a unified storage element (320), recovery maybe based on identities of allowed stripes. In this example, a solutionmay be more tolerant of different sets of storage devices beingavailable on subsequent recovery from prior interrupted recoveries. Forexample, a set of allowed commit identities may be defined to beassociated with data that is allowed in the storage system, and a set ofdisallowed commit identities may be defined to be associated with datathat is not allowed in the storage system for one or more reasons.

In this example solution, there may be several types of data, includingone or more of: erasure coded stripes, each of which is associated withat least one commit identity, recent commits of commit identities, a setof allowed commit identities, a set of disallowed commit identities, ora set of future commit identities.

Continuing with this example, when writing data into an erasure codedstripe, until that data is written completely, such that it isguaranteed to be recovered by any sufficiently large set of storagedevices available during a recovery, a storage controller may notconsider the data committed. Such a protocol that delays data as beingcommitted may include waiting until all shards of, say, an N+R stripeare fully written before anything in that N+R stripe can commit. In thiscase, there may be only one commit identity associated with the stripe,where the commit identity may be stored somewhere in the stripe thatshould be available during recovery. If parts of a stripe can be madedurable and recoverable without making the entire stripe durable andrecoverable, as suggested in previous sections, such as by writingsub-regions of shards with matching dual parity, or by mirroring writesto fast memory or by writing partial M+R shards of an N+R stripe (or anyof the other techniques described previously), then commit identitiesmay be associated with those partial elements of a stripe, but then therest of this argument applies to that partial stripe which must still becompletely persisted before proceeding to committing that partialstripe.

Further in this example, before any such written data may be relied upon(before it is considered recoverable, durable content of the storagesystem), the commit identities associated with the written data must bewritten down to ensure they will end up in the set of allowed commitidentities. The set of allowed commit identities may be written intoheaders for subsequent shards, or the set of allowed commit identitiesmay be written into fast storage on storage devices. In other cases,with regard to the storage devices above, the sets of allowed ordisallowed commit identities may be stored in memory mapped durablestorage on storage devices, or written to durable registers on storagedevices. Writing the sets of allowed or disallowed commit identitiesinto headers for subsequent shards may depend on waiting for sufficientnew data to trigger that new data being persisted into new shards. Insome examples, writing commit identities directly into fast memory, ormemory mapped durable storage, or durable registers may be done quickly,allowing written data to be considered durably recoverable more quickly.

For data to be considered reliably committed, such that the storagesystem implementation may continue operating in reliance upon the datahaving been reliably committed, associated commit identities for thedata considered reliably committed must be written to at least R+1storage devices, though commit identities could be written to moredevices than R+1 before proceeding.

In some implementations, recovery of recently written data may be basedupon identifying commit identities for the data being recovered, andupon determining whether those commit identities were written out ascommitted. In this example, if the identified commit identities aredetermined to be committed, then the data has been written completelyand may be safely considered to have been committed. In other words, ifthe identified commit identities are determined to be committed, thenthe data may be recoverable regardless of which R or fewer subsets of anN+R stripe are not available. To ensure that a subsequent recovery willalso see the commit identities as committed (possibly using a differentsubset of available storage devices), recovered commits of commitidentities should be written down again, if they had not already beenwritten to a sufficient number of storage devices to ensure they arerecoverable.

In some examples, a situation may arise where a recovery is unable toidentify the commit records for a set of commit identities. In oneexample solution, during a given recovery process, if the given recoveryprocess determines the list of commit identities whose commits the givenrecovery process did not find, then that list of commit identities maybe written into a disallowed list, where the disallowed list explicitlyremoves a commit identity from being considered valid content for thestorage system, or where the disallowed list stores an indication thatthe commit identity is invalid. More specifically, a recovery processmay generate two lists: (a) the allowed list that represents the set ofall commit identities which represent valid content for the storagesystem, and (b) the disallowed list that represents a set of commitidentities that specifically do not represent valid content for thestorage system. Once both the allowed list and the disallowed list havebeen determined, the allowed list and the disallowed list may be writtenout as new data, in some cases represented by a set of new commitidentities and committed by writing those new commit identities to asufficient number of storage devices.

Continuing with this example, one solution to determine the set ofcommit identities to add to the disallowed set is to determine whichcommit identities exist in written data but that lack persisted commitsof those commit identities found during recovery. Another examplesolution to determine the set of missing commit identities is toestablish, during operation of the storage system, a set of allowedfuture commit identities, and to make the set of allowed future commitidentities durable before any of those commit identities can be used forwriting new data. This results in three lists: (a) allowed commitidentities, (b) disallowed commit identities, and (c) potential futurecommit identities. All three lists can be written together orseparately, where the three lists may be associated with commitidentities for the writes that persist the lists, and committed bywriting commit records for those commit identities to a sufficientnumber of storage devices. In some examples, determining commitidentities to add into the disallowed list during recovery may includereading the last committed future commit identities list (where thecommitted future commit identities list may depend upon finding the lastsuch list for which a commit record could be recovered), determining thesubset of the committed future commit identities list for which nocommit record was found, and then adding that subset to the list ofdisallowed commit identities.

In this example, the lists of allowed, disallowed, and future commitidentities can be simplified by making commit identities sequential.During normal operation that does not include faults and correspondingrecoveries, the future commit identities list may be described as, ormay indicate, a range of numbers from some already used and fullycommitted number to some future number that has not yet been fullycommitted. At storage system or storage controller startup or after arecovery, the start of that range of numbers may be set to some valuethat must be past any commit identity that might have been missed due anunavailable storage device. Before the sequence numbers within the rangehave all been used, a new range may be written, where the process ofwriting the new range may use and commit at least one commitidentity—consequently, the new range should be written out prior tousing the last sequence number within the current range. Further, asdata is written, committed, and the data commits are added to theallowed list, the beginning of the range may be advanced to a commitidentity prior to any still in progress write and commit.

Continuing with this example, the use of ranges of sequentialidentifiers may also simplify the allowed and disallowed lists. Forexample, during recovery, any commit identity not already on thedisallowed list that precedes the first number on the future commitidentity range may be considered allowed—unless the commit identity wasalready disallowed such as in a previous incomplete recovery. Further,even if one of those commit identities had never been used, the commitidentity could not have been associated with partially written andcommitted data, which creates opportunities for compacting allowed anddisallowed lists into a set of ranges. In this example, the set ofcommit identities between the start of the future commit identitiesrange and one prior to the first number in the range for which a commitrecord of a commit identity is found is disallowed, creating one range.Further, the set of commit identities between one after the last numberin the future commit identity range for which a commit record of acommit identity is found and the last number in the future commitidentity range itself is also disallowed, thereby creating anotherrange. In some cases, other commit identities in the future commitidentity range may produce a messier set which may include individualcommit identities that are allowed or disallowed, or potentiallycompressible subranges where all commit identities in a range areallowed or disallowed.

To complete this example, an additional step or consideration for usingranges may be to track instances of encountering an error while writingout data prior to the data being committed. For example, if an erasurecoded partial or complete stripe cannot be written out completely due,for example, to an erase block or storage device failure, then thecontent may be rewritten to new partial or complete stripes on a new setof devices. In this case, the first failed attempt at writing the datashould be invalidated, and this invalidation may be done by adding anycommit identities associated with the failed writes to the disallowedlist. Otherwise, the simple range model described in the previousparagraph may fail to disallow the incomplete data on a recovery andmight add the data to the allowed list.

In some implementations of a unified storage element (320), the data andrecovery models may be implemented without use of multiple addressablestorage classes—where some storage class activity may be performedinvisibly by the storage system. However, given that a unified storageelement (320) may include fast durable storage and bulk durable storage,providing multiple addressable storage classes may provide a good basisfor speeding up data storage and system recovery, and a good basis formaking data storage and system recovery work better with flash storagethan it would without multiple addressable storage classes. Examples areprovided below.

As one example, as mentioned previously, commit identities may becommitted by writing them to memory mapped durable storage, fast durablestorage, or durable registers on a sufficient number of storage devices.Additionally, transferring data for a partial or complete stripe to fastdurable storage may allow a staging of data to a storage device beforeit is transferred to bulk storage, where operation of that bulk storagemight rely on restricted modes of operation to get the best performanceor the longest storage device lifespan. Further, allowed, disallowed,and future commit identities, as discussed above, particularly futurecommit identities, may be implemented by writing these different typesof identity data to fast durable storage. Particularly, in some cases, arange of future commit identities may be written as two numbers, andpossibly stored in durable mapped storage or durable registers, therebyallowing any identity within a range (of perhaps a few hundred potentialcommit identities) to be used, and allowing the future range to beextended or moved by writing down one or two numbers to alter thebeginning or end of the range.

As another example, reorganizing or reformatting of data stored in fastdurable storage as it is transferred to bulk storage may not requireadditional commit phases because the data may, with high probability,already be guaranteed to be on a sufficient number of storage devicesbefore a storage controller instructs it to reorganize or reformat thedata during transfer.

As another example, models of mirroring content to multiple storagedevices prior to converting it to, say, a RAID-6 format, or models ofwriting M+R content prior to transforming it into N+R content may needto associate commit identities with M+R content. In this example, forany other partial committable write of a complete erasure coded stripeto commit, it will have at least one commit identity associated with thecontent so that it can operate through the commit algorithm. Further,the recording of a commit identity could itself be written as asubsequent partial committable write even to the same erasure codedstripe.

In some implementations of a unified storage element (320), the featuresof a unified storage element (320) may be applied to various redundancymodels. Further, storage devices may also have internal redundancy, orstorage controllers may store redundant data within storage devices tohandle localized failures of individual blocks, erase blocks, or chips.Most of the following examples consider storage devices that failcompletely, or that do not power up and make themselves available in atimely fashion. However, in some cases, some data in, say, an N+R stripemight be unavailable due to a failed or corrupted read within anotherwise operating and available storage device. Further, internalredundancy may reduce the number of cases where this becomes a problem,to the point that it is statistically implausible, but internalredundancy may also fail, and some storage devices may not deploy enoughof it to recover from all non-catastrophic failure modes.

Continuing with this example, handling such failures may be solved by animplementation that avoids coming online if exactly R devices do notboot properly, and the implementation may instead wait to come onlineuntil the number of unavailable storage devices drops to R−1 or fewer.Alternately, an implementation might determine the recoverability of alldata that might have been in flight at the time of the fault thatpreceded recovery, and then ensure that none of the data encountered anunrecoverable error before proceeding to making changes that mightaffect subsequent recoveries. As an alternative, if a latent corruptionis encountered in a block for which at least one additional (but notcurrently available) redundancy shard might have been written, thestorage system may pause or fault waiting to determine if a storagedevice for that redundant shard eventually comes back online.

Further, note that storage systems may use various schemes for differentdata. For example, some data may be written as two mirrored copies thatare safe from one failure, other data might be written in a RAID-5 styleN+1 scheme, and other data might be written in a RAID-6 style N+2scheme, or even using three or four mirrored copies or using N+3 schemesfor triple failure redundancy (perhaps for critical but low-volumemetadata). Further, different data might be striped or mirrored acrossdifferent subsets of devices, where some subsets may overlap in variousways and other subsets might not. Any statements about the interrelationbetween complete, recoverable, incomplete, and unrecoverable data shouldthen consider the completion and recoverability model associated witheach type of data, so for example if content of a RAID-6 stripe followsand depends on a supposedly completed RAID-5 or two-mirrored-copy write,then the RAID-6 stripe's dual-failure-redundant validity during recoverymay depend on the recoverability of single-failure-redundant content,regardless of how the set of storage devices for each written dataset door do not overlap.

Further, storage systems may divide up storage devices such thatredundancy operates within constrained groups of devices (possiblyorganized around natural divisions such as enclosures or internalnetworks and busses). Such constrained groups of devices may be calledpools, or write groups, or RAID groups, or referred to by other names.In this example, such a constrained group of devices is referred to as awrite group, where a principle behind a write groups is that any N+Rstripe that utilizes any storage device within the write group will onlystore shards on other storage devices in the same write group. Forexample, if there are 12 storage devices in write group A and 12 storagedevices in write group B, then any pairing of data and redundancy willbe constrained within either the 12 storage devices in write group A orthe 12 storage devices in write group B. In some cases, write groups fora storage system may be different sizes and not uniform sizes. Further,as storage is added, write groups may be extended to include morestorage devices, or additional write groups may be added to the storagesystem. In some cases, incremental addition of storage devices may causemaking a choice between making existing write groups too large or makinga new write group that is too small—in such a case, the storage systemmay split existing write groups and transform existing content to matchthe new write group layouts. These constraints may limit damage causedby failure of devices by limiting the intersections of failed devicesacross all stripes. As an illustrative example: if two devices failed inwrite group A and one device failed in write group B, no N+2 stripecould encounter all three failed devices while writing out stripes orduring recovery because any stripe stored in write group A which mightinclude write group A's two failed devices will not include the failedstorage device in write group B, and no stripe in write group B whichmight include write group B′s one failed device will not include eitherof the two failed storage devices in write group A.

To complete this example, in a storage system that implements writegroups or some similar constraint in allocating shards of redundantdata, previous discussions concerning numbers of failed storage devicesthat allow continued operation or a successful recovery should applythose rules to such individual groups of storage devices, rather than tothe entire storage system.

In some additional implementations, erasure coding of staged data tofast durable storage and to bulk storage may be performed within astorage system. For example, consider a storage system with two or morecontrollers, where each controller is connected to a plurality ofstorage devices, where multiples ones of the storage devices areconnected to one or more of the storage controllers, and where at leastsome of storage devices include both (1) addressable fast durablestorage that supports a high transfer rate or a predictable low latencyor a high and sustained overwrite rate or a combination of suchproperties, and (2) addressable bulk storage that may be substantiallylarger than the addressable fast durable storage or may not support highoverwrite rates without degradation of storage lifespan or might haveworse or less predictable latency or some combination of theseproperties. In this example, each type of storage is addressable andseparately accessible for at least read and write from each of theirconnected storage controllers, and where the storage devices may furthersupport commands issued from connected storage controllers to transfercontent to other storage devices possibly from the addressable fastdurable storage and possibly from the addressable bulk storage, or mayfurther support commands to transfer specific content from fast durablestorage to bulk storage, possibly with some transformations to format orcalculated erasure codes.

In some implementations, some functions of a storage device or of astorage controller may be combined or co-resident on unified elements(i.e., removable elements or circuit boards) of a storage system, whereone or more of the storage controller functions on one such element maycommunicate as described herein, and where storage device functions maybe implemented on separate such elements.

For example, a storage controller may persist state associated withoperations that should be persisted quickly, where that persisted stateis at least the minimum needed to ensure recoverability of the operationin cases of faults due to software, hardware, or power loss, to fastaddressable storage across multiple storage devices to ensurereliability against faults, and where the persisted state is written asa dynamically allocated erasure coded stripe or simple mirroredreplicas, in patterns that allow recovery in the face of faults in up toR₁ storage devices. In this example, the operation may be completed tobulk storage by writing whatever content remains after deduplication,compression, or other possibly invalidating operations (for exampleoverwrites, deletions, unmaps/trim calls, virtual copy operations) to anerasure coded stripe formed across the bulk storage of multiple storagedevices, where the erasure coded stripe allows recovery in the face offaults in up to R₂ storage devices.

In this example, R₁ and R₂ may be the same. For example, if a RAID-6scheme involving P and Q parity shards according to typical RAID-6models of erasure coding is used for storing both fast write content tofast durable storage and longer-duration content to bulk storage, R₁ andR₂ might both be 2. Likewise, with a RAID-5 scheme, both R₁ and R₂ mightbe 1. Other schemes such as those involving Galois fields can support R₁and/or R₂ of 3 or more. The data shards associated with content writtento fast durable storage might be one number, say M, such that writes offast content comprise M+R₁ stripes written to dynamically allocatedshards across addressable fast durable storage of M+R₁ storage devices,while data shards associated with content written to bulk storage mightbe another number, say N (which may be the same or different number asM) such that writes of longer-duration content comprise N+R₂ stripeswritten to shards across addressable bulk storage of N+R₂ storagedevices.

In some implementations, the writing of content to either fast durablestorage or bulk durable storage need not be written as uniform-widtherasure coded stripes. For example, some data written to fast durablestorage might be written as M₁+R₁ while other data might be written asM₂+R₁ where M₁≠M₂. Similar variations might apply to data written tobulk storage. Further, if less than a full stripe's worth of data isavailable to be written, fewer shards (or subregions of complete shards)might be written, together with intermediate calculated redundancycontent (e.g., P or P and Q shards for RAID-5 or RAID-6 ) calculatedfrom the data so far available. As further data is ready to be stored,additional shards (or additional subregions of complete shards) might bewritten together with updated redundancy content. Further in thisexample, partially written content to bulk storage might result inmatching intermediate redundancy content being written first toaddressable fast durable storage (separately from any redundancy schemeassociated purely with writes of data content to fast durable storage)with a final version of redundancy content written to bulk storage ascompleted shards of redundancy when complete corresponding data shardshave been fully written.

In some implementations, if content written to fast durable storage issufficiently similar to content to be written to bulk storage, then oneor more storage controllers may utilize capabilities of storage devicessuch that the content to be stored in the addressable bulk storage for aspecific storage device is first directed to fast durable storage on thesame storage device (or alternately, content stored in fast durablestorage is eventually stored into bulk storage on the same storagedevice), and then transferred (perhaps with some reformatting) from fastdurable storage to bulk durable storage directly by the storage device,under direction from operations running on the one or more storagecontrollers (using the previously described commands to transfer andpossibly reformat content).

In some implementations, if content written to fast durable storage issimilar to content that is written to bulk storage on a separate storagedevice, then if the storage devices and interconnect support it, and acommand is available to do so, and there is efficiency to be gained indoing so (such as due to relative availability of interconnectbandwidth), one or more storage controllers may direct storage devicesto transfer (and possibly reformat) content between the fast durablestorage on a first, source storage device and the bulk storage of asecond, target storage device.

In some example implementations, it may be possible for data to transferbetween a first storage device and a second storage device such that thetransferred data coupled with data already present on the second storagedevice (such as in either fast durable storage and bulk storage orpossibly transferred from a third or further additional storage devices)can be used to calculate new data to be stored, such as combiningpartial data into combined formatted data, or such as calculatingcontent for redundancy data shards from prior intermediate content forredundancy data shards coupled with different intermediate content forthe redundancy data shards or content from data shards. For example,with a simple XOR parity-based redundancy shard, a first partial paritycalculated by XOR'ing data from a first subset of data shards and asecond partial parity calculated by XOR'ing data from a second subset ofdata shards can be transferred from a first storage device whichcalculated the first partial parity and from a second storage devicewhich calculated the second partial parity to a third storage devicewhich can then XOR the first and second partial parties together toyield a complete calculated parity which can be stored into bulk durablestorage within the third storage device.

Further, in some implementations, Galois field math allows similarpartial results to be merged together to store additional types ofcalculated redundancy shards, such as the typical Q shard for a RAID-6stripe. For example, with the Galois math described in the paper “Themathematics of RAID-6 ” by H. Peter Anvin, 20 Jan. 2004, consider thatthe final Q shard for a 5+2 RAID-6 stripe is calculated as:

Q=g ⁰ ·D ₀ +g ¹·D₁ +g ²·D₂ +g ³·D₃ +g ⁴·D₄

In this example, a calculation of a partial Q from just the first twodata shards could be calculated as:

Q _(p1) =g ⁰·D₀ +g ¹·D₁

In this example, Q_(p1) may be stored by a storage controller on somefirst partial Q shard storage device as part of protecting just thefirst two data shards for the eventual end resulting 5+2 stripe, andthis plus an additional XOR parity stored in yet another storage deviceis enough to recover from any two faults of the devices written for thispartial stripe, since as long as the partial stripe is properlyrecognized as partial, the content from g²·D₂+g³·D_(3+g) ⁴ ·D₄ can beinferred to be calculated from empty (zero) data shards D₂, D₃, and D₄.

Continuing with this example, a second calculation of a partial Q fromthe other three data shards could further be calculated as:

Q _(p2) =g ² ·D ₂ +g ³ ·D ₃ +g ⁴·D₄

In this example, Q_(p2) may be stored on a second partial Q shardstorage device as part of protecting those three data shards, and aswith the first partial Q shard, when coupled with an additionallywritten partial XOR parity written to another storage device, thepartial content is again protected from any two faults since the contentfrom g⁰·D₀+g¹·D₁ associated with the partial Q shard can again beinferred to be calculated from empty (zero) data shards D₀ and D₁.

Continuing with this example, a storage device which eventually receivedboth Q_(p1) and Q_(p2) may calculate the Q value for the complete stripeincluding all five data shards as an appropriate Galois field additionof Q_(p1) and Q_(p2).

In some implementations of a unified storage element (320), the featuresof a unified storage element (320) may be applied to implement dataredundancy using commit identities, as described above, and asadditionally described as follows. For example, given a storage systemof the form described above, but where some or all storage devices (orthe storage device aspect of combined storage elements) may or may nothave separately addressable fast durable storage and bulk durablestorage, or where storage devices may or may not further support therecording of numbers in durable mapped memory or addressable durableregisters, various schemes may be used that are based at least in parton commit identities as described above to ensure corruption-freesequences of storage system and storage device faults and recoverieswhich are themselves interrupted by faults where subsequent recoveriesare subject to inconsistent sets of available, slow to become available,or faulted storage devices.

In these examples using commit identities, one consequence is to ensurethat within the context of a storage system based on a redundancy levelof R_(c) for all written content, incompletely written erasure codedstripes do not result in inconsistent actions taken by subsequentrecoveries in such a sequence of faults, recoveries and unavailablestorage devices. As one example, with a scheme that relies on threelists of commit identities, which may be efficiently represented asranges of numeric commit identities, which represent and correspond to:allowed (verified as durably committed) content, disallowed content, andpotential content that should be verified during a recovery. In thisexample, these three lists are content that is written to fast durablestorage, bulk durable storage, durable persistent mapped memory, durableregisters, or various combinations of these types of memory by usingmirroring or erasure coding with a redundancy level sufficient to handleRd faults, which generally matches the requirements for storage devicefault handling for the storage device itself. However, in otherexamples, different content may use different levels of redundancy (forexample, key metadata may be written with higher redundancy than bulkdata, or transactional content or fast write content may be written to asmaller number of devices that are distinct from devices used for bulkstorage). In general, R_(id) should be no less than the highest R_(c)value associated with any other storage system content. However, in someexamples, it is also possible that segregated storage devices will beused to record some or all updates to lists of commit identities, wherethe segregated storage devices (e.g., specialized NVRAM devices) operatewith a reduced redundancy level.

In some embodiments, storage system content (generally including commitidentity lists, though in some cases these commit identity lists may bean exception, particularly with durable registers that record ranges) iswritten to storage devices with associated commit identities, generallyinline with the content itself (such as being written within shards,pages, or segments that also store data or metadata that represents thedurable content stored by the storage system). In some examples, writtencontent may be associated with a commit identity that is in thepotential content list, where once completely written with the requiredlevel of redundancy across storage devices (e.g., N+R_(c) for typicalcontent, or R_(id)+1-way mirrored or N_(id)+R_(id) erasure coded forcontent storing lists), the associated commit identities may be added tothe allowed list, where the allowed list may be written out. In thisexample, after the allowed list update that includes a commit identityhas been completely written across storage devices with a correspondingredundancy, the storage system may be assured to be recoverable by anysubsequent recovery. Further in this example, if there is an issue inwriting content (such as due to a storage device fault while it is beingwritten), either matching content may be written to an alternate deviceor the commit identity associated with the content may be added to thedisallowed list and written out and the entire update can be performedagain as a fresh update to new locations and with new commit identities.However, in some examples, even though the writing of an allowed ordisallowed list may itself utilize commit identities from the pendinglist for the writing of the list itself, and even though general contentwrites depend on associated commit identities being added to the allowedlist and written out, after an allowed list is written out with requiredredundancy, the associated content is committed without the allowed listupdate being further committed with an additional update to the allowedlist (otherwise, there may be an infinite progression in order to commitdata).

In some implementations, during recovery, storage system content may bescanned to reconstruct the allowed commit identity list, the disallowedcommit identity list, and the potential commit identity list. Entriesadded to the potential commit identity list may be confirmed bydetermining whether the potential commit identity list update wascommitted, which can be based on the use of a previously committed andconfirmed entry in the potential commit identity list. In this example,a commit identity in the recovered potential commit identity list may betested for whether it may have been associated with an addition to theallowed list. Further in this example, even a partial update adding anidentity to the allowed list should exist only if the associated contenthad been completely written and completed as a fully redundant mirroredor erasure coded stripe, so it is safe to conclude that such a commitidentity may be safely recovered into the allowed list even if it wasonly partially written out with the required redundancy. However, inthis example, an identity in the recovered potential commit identitylist for which there is no verifiable entry added to the allowed list,even as a partially written update (or possibly in the case of an updatewhich was clearly only partially written), can be added to thedisallowed commit identity list. In this example, to handle the case ofone recovery failing to find a partially written allowed commit identityand a subsequent recovery that does find the partially written allowedcommit, the second recovery may use the existence of the commit identityin the disallowed list to overrule the addition of the commit identityto the confirmed allowed list.

In some implementations, as part of a recovery that depends on the useof confirmed written data, the potential commit identity list should beresolved into a list of committed identities because they showed up inan at least partially written allowed list update, with all otherpotential commit identities (including any potential commit identitiesthat might possibly have been partially written) resolved into thedisallowed list. Further, in this example, prior to a recovery processacting on the results of any such content, the allowed and disallowedlist updates should be written out and committed. In this example, atthis point, any written content associated with a commit identity may bedetermined to have been allowed (it was fully written and committed) orto be disallowed (it may or may not have been fully written, but it hadnot been successfully and completely committed and was backed out beforethat had completed). Further, in this example, a partially writtenupdate to an allowed list may end up disallowed in a sequence ofrecoveries and faults, where in such a case, such an update to theallowed list ends up being discarded because the update is not allowed.In this way, in this example, sequences of faults and recoveries mayprevent missing updates in one recovery from infecting a subsequentrecovery that observes the update.

In some implementations, during the normal running of a storage system,it is possible that writes of erasure coded content may not completesuccessfully due to, for example, a fault affecting writes or inreceiving status for a write completion. In such cases, entries may beadded to a disallowed list to effectively negate the associated writtencontent (by disallowing its related commit identity).

In some implementations, during normal operation, updates to lists ofcommit identities may be persisted by writing into successive updatesthat follow the content that the commit identity is intended to commit.In some examples, alternately, or additionally, some updates to lists ofcommit identities, or some additions of ranges of identities to thepotential commit identity list or to the allowed commit identity list,may be written to durable registers or to durable memory interfaces onsupported storage devices. In some examples, support for up to R devicefaults can be supported either by writing an M+R erasure code containingthese list updates, or by storing R+1 copies of the list updates (orranges) on R+1 separate supported storage devices. An R+1 scheme isparticularly suitable with durable registers or durable memory.

For further explanation, FIG. 4 sets forth a flow chart illustrating anexample method for recovering data on a storage element integrating fastdurable storage and bulk durable storage, such as unified storageelement (320), according to some embodiments of the present disclosure.Although depicted in less detail, the unified storage element (320) maybe similar to the storage systems described above with reference toFIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3C, or any combination thereof. Infact, the storage element, such as unified storage element (320) mayinclude the same, fewer, additional components as the storage systemsdescribed above.

As described above, a unified storage element (320) integrates fastdurable storage (454) and bulk durable storage (456). In this example,the unified storage element (320) receives a data storage operation(452), such as a write operation for one or more portions of data (460).As described above, commit identities may be used for determiningcommitted portions of data across one or more system reboots, whereacross each system reboot, different sets of data may be readable due toinconsistent sets of storage devices being available during a givensystem boot.

The example method depicted in FIG. 4 includes receiving (402), at astorage device (320) comprising fast durable storage (454), multipleportions of data (460) from a host computer (450). In this example, ahost computer (450) may be a remote computing device such as a remotedesktop, a mobile device, a virtual machine within a cloud computingenvironment, or some other type of computing device or instanceexecuting either locally within a storage system or at a geographicallyremote location. Receiving (402), at a storage device comprising fastdurable storage (454), multiple portions of data (460) may beimplemented by the unified storage element (320) receiving a message,such as a write operation and corresponding data payload, at acommunication port connected to a storage area network (158), a localcommunication interconnect (173), or a local area network (160), such asdepicted in FIG. 1A, in accordance with one or more networkcommunication protocols.

The example method depicted in FIG. 4 also includes storing (404),within the fast durable storage (454) of the storage device (320), oneor more commit identities (462) for one or more of the multiple portionsof the data (460) that have been successfully written. Storing (404),within the fast durable storage (454) of the storage device (320), oneor more commit identities (462) for one or more of the multiple portionsof the data (460) that have been successfully written may be implementedby one or more controllers of the unified storage element (320), asdescribed above with regard to creating and maintaining commitidentities for tracking successfully written portions of data.

The example method depicted in FIG. 4 also includes, responsive to asystem recovery, reading (406) the one or more commit identities (462)from the fast durable storage (454) to determine a set of valid portionsof the data (460). Reading (406) the one or more commit identities (462)from the fast durable storage (454) to determine a set of valid portionsof the data (460) may be implemented by one or more controllers of theunified storage element (320), as described above with regard to usingcommit identities for determining a set of valid portions of data.

For further explanation, FIG. 5 sets forth a flow chart illustrating anexample method for recovering data within a unified storage element(320) according to some embodiments of the present disclosure. Theexample method depicted in FIG. 5 is similar to the example methoddepicted in FIG. 4, as the example method depicted in FIG. 5 alsoincludes: receiving (402), at a storage device (320) comprising fastdurable storage (454), multiple portions of data (460) from a hostcomputer (450); storing (404), within the fast durable storage (454) ofthe storage device (320), one or more commit identities (462) for one ormore of the multiple portions of the data (460) that have beensuccessfully written; and responsive to a system recovery, reading (406)the one or more commit identities (462) from the fast durable storage(454) to determine a set of valid portions of the data (460).

However, the example method depicted in FIG. 5 differs from the examplemethod depicted in FIG. 4 in that FIG. 5 also includes transferring(502) successfully written portions of the data (460) from the fastdurable storage (454) to bulk durable storage (456). Transferring (502)successfully written portions of the data (460) from the fast durablestorage (454) to bulk durable storage (456) may be implemented by one ormore controllers of the unified storage element (320), as describedabove with regard to transferring data from fast durable storage intobulk durable storage based on one or more events or conditions withinthe unified storage element (320).

Example embodiments are described largely in the context of a fullyfunctional computer system for synchronizing metadata among storagesystems synchronously replicating a dataset. Readers of skill in the artwill recognize, however, that the present disclosure also may beembodied in a computer program product disposed upon computer readablestorage media for use with any suitable data processing system. Suchcomputer readable storage media may be any storage medium formachine-readable information, including magnetic media, optical media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsof the method as embodied in a computer program product. Persons skilledin the art will recognize also that, although some of the exampleembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Readers will appreciate that the steps described herein may be carriedout in a variety ways and that no particular ordering is required. Itwill be further understood from the foregoing description thatmodifications and changes may be made in various embodiments of thepresent disclosure without departing from its true spirit. Thedescriptions in this specification are for purposes of illustration onlyand are not to be construed in a limiting sense. The scope of thepresent disclosure is limited only by the language of the followingclaims.

What is claimed is:
 1. A method of recovering data within a unifiedstorage element, the method comprising: receiving, at a storage devicecomprising fast durable storage, multiple portions of data from a hostcomputer; storing, within the fast durable storage of the storagedevice, one or more commit identities for one or more of the multipleportions of the data that have been successfully written; and responsiveto a system recovery, reading the one or more commit identities from thefast durable storage to determine a set of valid portions of the data.2. The method of claim 1, wherein the multiple portions of data areshards of a RAID stripe.
 3. The method of claim 1, wherein the one ormore commit identities for the one or more of the multiple portions ofdata are written into headers for subsequent shards.
 4. The method ofclaim 1, wherein the one or more commit identities are written into oneor more of: memory mapped durable storage or durable registers.
 5. Themethod of claim 1, further comprising: transferring successfully writtenportions of the data from fast durable storage to bulk durable storage.6. The method of claim 1, wherein prior to a portion of data beingcommitted, the portion of data is included within a list of disallowedcommit identities, and subsequent to the portion of data beingcommitted, the portion of data is included within a list of allowedcommit identities and removed from the list of disallowed commitidentities.
 7. The method of claim 6, wherein determining the set ofvalid portions of the data is dependent upon the list of allowed commitidentities and the list of disallowed commit identities.
 8. An apparatusfor recovering data, the apparatus comprising a computer processor, acomputer memory operatively coupled to the computer processor, thecomputer memory having disposed within it computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of: receiving, at a storage device comprising fastdurable storage, multiple portions of data from a host computer;storing, within the fast durable storage of the storage device, one ormore commit identities for one or more of the multiple portions of thedata that have been successfully written; and responsive to a systemrecovery, reading the one or more commit identities from the fastdurable storage to determine a set of valid portions of the data.
 9. Theapparatus of claim 8, wherein the multiple portions of data are shardsof a RAID stripe.
 10. The apparatus of claim 8, wherein the one or morecommit identities for the one or more of the multiple portions of dataare written into headers for subsequent shards.
 11. The apparatus ofclaim 8, wherein the one or more commit identities are written into oneor more of: memory mapped durable storage or durable registers.
 12. Theapparatus of claim 8, wherein the computer program instructions that,when executed by the computer processor, cause the apparatus to carryout the steps of: transferring successfully written portions of the datafrom fast durable storage to bulk durable storage.
 13. The apparatus ofclaim 8, wherein prior to a portion of data being committed, the portionof data is included within a list of disallowed commit identities, andsubsequent to the portion of data being committed, the portion of datais included within a list of allowed commit identities and removed fromthe list of disallowed commit identities.
 14. The apparatus of claim 13,wherein determining the set of valid portions of the data is dependentupon the list of allowed commit identities and the list of disallowedcommit identities.
 15. A computer program product for recovering data,the computer program product disposed upon a computer readable medium,the computer program product comprising computer program instructionsthat, when executed, cause a computer to carry out the steps of:receiving, at a storage device comprising fast durable storage, multipleportions of data from a host computer; storing, within the fast durablestorage of the storage device, one or more commit identities for one ormore of the multiple portions of the data that have been successfullywritten; and responsive to a system recovery, reading the one or morecommit identities from the fast durable storage to determine a set ofvalid portions of the data.
 16. The computer program product of claim15, wherein the multiple portions of data are shards of a RAID stripe.17. The computer program product of claim 15, wherein the one or morecommit identities for the one or more of the multiple portions of dataare written into headers for subsequent shards.
 18. The computer programproduct of claim 15, wherein the one or more commit identities arewritten into one or more of: memory mapped durable storage or durableregisters.
 19. The computer program product of claim 15, wherein priorto a portion of data being committed, the portion of data is includedwithin a list of disallowed commit identities, and subsequent to theportion of data being committed, the portion of data is included withina list of allowed commit identities and removed from the list ofdisallowed commit identities.
 20. The computer program product of claim19, wherein determining the set of valid portions of the data isdependent upon the list of allowed commit identities and the list ofdisallowed commit identities.